With GPT-5, generative artificial intelligence has reached a new level. Performance is improving, use cases are multiplying, and in many organizations, conversational AI is becoming an operational staple of daily life: customer relations, internal support, document automation, and business assistance. But this acceleration raises a question: where is the data processed, who retains control over it, and under what legal framework?
Behind the power of these models, one reality remains: the technologies most widely used today are, for the most part, developed and operated outside Europe. For French companies, the stakes go beyond the question of performance. They touch on economic sovereignty, the law, and the ability to maintain control over components that have become fundamental to their operations.
Data and Dependency: The Risk of Technological Lock-in
Entrusting sensitive data flows to non-European platforms is never without risk. This can lead to transfers outside the European Union, expose companies to foreign laws, and increase dependence on actors whose priorities do not always align with European data protection requirements.
In many projects, the issue of data hosting and control comes up too late. This is due to a lack of time, but also because certain solutions have become established as standards. Yet generative AI relies on business, customer, or internal data that has become a strategic asset.
As these technologies become integrated into critical processes, another challenge emerges: technological lock-in. The more central a model becomes, the harder it is to move away from it. Companies rely on specific APIs, adapt their processes, train their teams on an ecosystem, and integrate these tools into their customer journeys.
This dependency can, over time, reduce their bargaining power and their technical or budgetary flexibility. For a long time, the market prioritized performance over control. But as AI becomes an operational infrastructure, data governance and freedom of technological choice are once again becoming decisive criteria.
Maintaining control is not just about choosing one provider over another, but also involves building expertise. Many companies still perceive AI as a complex black box, difficult to manage, oversee, or audit. Strengthening internal expertise is therefore becoming a strategic priority to build teams capable of governing data, evaluating models, and structuring robust use cases. Autonomy begins with the ability to understand what is being deployed, not just to consume tools.
Rebalancing with French and European solutions
Dependence on major international models is not inevitable. The French and European ecosystem is advancing rapidly, with solutions capable of meeting concrete needs while offering better regulatory and cultural alignment.
Players like Mistral AI embody a new generation of models developed in Europe, with a more open approach designed for industrial use. In Germany, Aleph Alpha is working on systems tailored to sensitive environments, where traceability and transparency are essential. And on the infrastructure side, groups like OVHcloud remind us that there are also credible alternatives for hosting and scaling AI applications without relying exclusively on major international cloud providers.
These initiatives do not replace GPT-5. Rather, they allow for the reintroduction of choice, customization, and above all, greater control over data and terms of use. Technological sovereignty rests on the ability to diversify and manage dependencies.
Building a collective, industrial, and European response
With the gradual implementation of the AI Act, companies must now better structure their artificial intelligence projects: document systems, clarify responsibilities, and strengthen governance. This framework comes after a phase of rapid experimentation, where innovation often preceded organization.
But these challenges extend beyond the scope of any single company. France, like Europe, has often approached major technological transformations in a fragmented manner. AI, on the contrary, demands a collective response: industrial alliances, public-private partnerships, support for European players, and the development of infrastructure capable of supporting its industrialization.
The talent exists, the research is solid, and many companies are gaining momentum. The challenge now is to transform these assets into sustainable industrial capacity. The question is not about choosing between GPT-5 and European alternatives, nor about pitting openness against sovereignty.
The challenge for French companies is to retain the ability to decide: where their data goes, which technological building blocks they rely on, and to what extent they are willing to delegate functions that have become essential.
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